| Challenge: | Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency. |
| Approach: | They propose a context rewriting method which explicitly rewrites the last utterance by considering context history. |
| Outcome: | The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots. |
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| Challenge: | Recent research has achieved impressive results in single-turn dialogue modelling, but multi-turn models still remain challenging. |
| Approach: | They propose to rewrite human utterances as a pre-process to help multi-turn dialgoue modelling. |
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Explicit Query Rewriting for Conversational Dense Retrieval (2022.emnlp-main)
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| Challenge: | In a conversational search scenario, a query might be context-dependent because some words are referred to previous expressions or omitted. |
| Approach: | They propose a model that performs query rewriting and context modelling in a unified framework by highlighting relevant terms in the query context. |
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Beyond Goldfish Memory: Long-Term Open-Domain Conversation (2022.acl-long)
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| Challenge: | Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. |
| Approach: | They propose to use retrieval-augmented methods to summarize and recall past conversations to improve their models. |
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CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2022.emnlp-main)
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Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar
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ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
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Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)
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| Challenge: | Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems. |
| Approach: | They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete . |
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Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)
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| Challenge: | Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues. |
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Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)
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| Challenge: | Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses. |
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Modeling Multi-turn Conversation with Deep Utterance Aggregation (C18-1)
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| Challenge: | Existing work on retrieval-based context modeling for multi-turn conversation ignores interactions among previous utterances. |
| Approach: | They propose retrieval-based response matching for multi-turn conversation . they propose to combine previous utterances into context using a deep utterrance aggregation model . |
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Can You Unpack That? Learning to Rewrite Questions-in-Context (D19-1)
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| Challenge: | Existing QA datasets lack key NLP problems like coreference and ellipsis resolution. |
| Approach: | They propose a task of question-in-context rewriting to rewrite a context-dependent question into a self-contained question with the same answer. |
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